پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شمارهها | 675 |
| تعداد مقالات | 9,848 |
| تعداد مشاهده مقاله | 69,911,399 |
| تعداد دریافت فایل اصل مقاله | 49,224,540 |
Optimization of the dynamic network data envelopment analysis (DNDEA) model based on the game theory in the capital market | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 29، دوره 14، شماره 8، آبان 2023، صفحه 327-341 اصل مقاله (526.52 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.28714.3975 | ||
| نویسندگان | ||
| Ali Asghar Tatlari1؛ Seyed Jafar Sajjadi* 2؛ Roya Soltani3 | ||
| 1Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 2Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran | ||
| 3Department of Industrial Engineering, Faculty of Engineering, khatam University, Tehran, Iran | ||
| تاریخ دریافت: 25 شهریور 1401، تاریخ بازنگری: 13 آبان 1401، تاریخ پذیرش: 15 آبان 1401 | ||
| چکیده | ||
| Data envelopment analysis (DEA) is a widely-used method in measuring the relative efficiency of sets of homogeneous decision-making units with the same inputs and outputs. In classical DEA models, the whole system is usually considered as a decision-making unit (DMU) to calculate its efficiency, and the communication of separate processes within the system is ignored. However, the internal communication of different sectors of a decision-making unit can have various structures that cause complexity in evaluating its efficiency. To this end, the Network DEA (NDEA) model is developed using communication variables to communicate the internal structures of the decision-making units, in which the production process has two or more stages and considers according to the communication of the internal sectors and sub-units of a decision-making unit as a network structure, and the efficiency of each internal process and the whole process is calculated independently. Therefore, the present research aimed to evaluate the efficiency and ultimately rank the decision-making units and it thus used the dynamic network data envelopment analysis (DNDEA) model using game theory to examine and solve a problem of capital market investors, which was the correct selection of stocks in the efficient companies compared to the stocks of companies with low-efficiency. After developing the proposed model, the performance of 25 active companies in the petrochemical industry was evaluated for three years of 2016-2018 and the efficiency of each stage was calculated along with their overall efficiency. The results indicated that the proposed model had solved the shortcomings of the previous models and the new approach to the evaluation of efficiency in the stock market could provide a more accurate understanding of the performance and efficiency of active companies in this field. | ||
| کلیدواژهها | ||
| Dynamic network data envelopment analysis (DNDEA)؛ Game theory؛ Stock market؛ Cooperative and competitive strategy؛ GAMS | ||
| مراجع | ||
|
[1] M. Anisi, The use of linear programming in the joint relations of game theory and data envelopment analysis, Master’s thesis, Islamic Azad University, Science and Research Branch, Faculty of Basic Sciences, 2008. [2] N. Avkiran and A. McCrystal, Dynamic network range-adjusted measures. dynamic network slacks-based measure, J. Oper. Res. Soc. Japan 57 (2014), no. 1, 1–14. [3] R.D. Banker, A. Charnes, W.W. Cooper and R. Clarke, Constrained game formulations and interpretations for data envelopment analysis, Eur. J. Oper. Res. 40 (1989), no. 3, 299–308. [4] F. Canbek, Analysis of the efficiency of private hospitals in Istanbul with data envelopment analysis, Master Thesis, Anadolu University, Institute of Social Sciences, Eskisehir, 2007. [5] M. Chen, M. Chien and J. van Dalen, Measuring dynamic efficiency: theories and an integrated methodology, Eur. J. Oper. Res. 203 (2010), 749–760. [6] Y. Chen, W.D. Cook, N. Li and J. Zhu, Additive efficiency decomposition in two-stage DEA, Eur. J. Oper. Res. 196 (2009), 1170–1176. [7] A. Charnes, W. W. Cooper, B. Golany, R. Halek, G. Klopp, E. Schmitz and D. Thomas, Two phase data envelopment analysis approaches to policy evaluation and management of army recruiting activities: Tradeoffs between joint services and army advertising, Center for Cybernetic Studies, University of Texas-Austin Austin, Texas, USA, 1986. [8] A. Charnes, W.W. Cooper and E. Rhodes, Measuring the efficiency of decision-making units, Eur. J. Oper. Res. 2 (1978), no. 6, 429–444. [9] M. Chen and V.J. Dalen, Measuring dynamic efficiency: Theories and an integrated methodology, Eur. J. Oper. Res. 203 (2010), 749–760. [10] K.D. Despotis, D. Sotiros and G. Koronakos, A network DEA approach for series multi-stage process, Omega 61 (2016), 35–48. [11] R. Fare and S. Grosskopf, Network DEA, Socio Econ. Plan. Sci.34 (2004). [12] E. Feroz, S. Kim and R.L. Raab, Financial statement analysis: A data envelopment analysis approach, J. Oper. Res. Soc. 54 (2003), no. 1, 48–58. [13] H. Fukuyama and W.L. Weber, A slacks-based inefficiency measure for a two-stage system with bad outputs, Omega 38 (2010), 398–409. [14] H. Fukuyama and W.L. Weber, Japanese bank productivity, 2007- 2012: A dynamic network approach, Pacific Econ. Rev. 22 (2017), no. 4, 649–676. [15] J.S. Hahn, H.M. Sung, M.C. Park, S.Y. Kho and D.K. Kim, Empirical evaluation on the efficiency of the trucking industry in Korea, KSCE J. Civil Engin. 19 (2015), 1088–1096. [16] R. Jahangoshai, M. Moini and A. Makui, Operational and non-operational performance evaluation of thermal power plants in Iran: A game theory approach, Energy 38 (2012), 96–103. [17] C. Kao, Dynamic data envelopment analysis: A relational analysis, Eur. J. Oper. Res. 227 (2013), no. 2, 325–330. [18] C. Kao, Efficiency decomposition in network DEA: A relational model, Eur. J. Oper. Res. 192 (2009), no. 3, 949–962. [19] C. Kao, Network data envelopment analysis: A review, Eur. J. Oper. Res. 239 (2014), 1-16. [20] C. Kao and S.N. Hwang, Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan, Eur. J. Oper. Res.185 (2008), no. 1, 418–429. [21] C. Kao and S. N. Hwang, Efficiency measurement for network systems: IT impact on firm performance, Decision Support Syst.48 (2010), 437–446. [22] A. Lalar, Measuring the efficiency of municipalities with data envelopment analysis, Doctoral Thesis, Hacettepe University, Institute of Science and Technology, Ankara, 2003. [23] Y. Li, Y. Chen, L. Liang and J. Xie, DEA models for extended two-stage network structures, Omega 40 (2012), no. 5, 611–618. [24] L. Liang, J. Wu, W.D. Cook and J. Zhu, The DEA game cross-efficiency model and its Nash equilibrium, Oper. Res. 56 (2008), no. 5, 1278–1288. [25] S. Lozano, Alternative SBM model for network DEA, Comput. Ind. Engin. 82 (2015), 33–40. [26] X.M. Luo, Evaluating the profitability and marketability efficiency of large banks: An application of data envelopment analysis, J. Bus. Res. 56 (2003), 627–635. [27] R. Malhotra, D.K. Malhotra and P. Russel, Using data envelopment analysis to rate bonds, Proc. Northeast Bus. Econ. Assoc. 4 (200), 420–423. [28] P. Moreno and S. Lozano, Super SBI Dynamic Network DEA approach to measuring efficiency in the provision of public services, Int. Trans. Oper. Res. 25 (2016), no. 2, 715–735. [29] K. Nakabayashi and K. Tone, Egoist’s dilemma: a DEA game, Omega 34 (2006), 135–148. [30] J. Nemoto and M. Goto, Dynamic data envelopment analysis: Modeling inter-temporal behavior of a firm in the presence of productive inefficiencies, Econ. Lett. 64 (1999), 51–56. [31] J. Nemoto and M. Goto, Measurement of dynamic efficiency in production: An application of data envelopment analysis to Japanese electric utilities, J. Prod. Anal. 19 (2003), 191–210. [32] K.O. Oru，c, Efficiency measurements in a fuzzy environment with data envelopment analysis and an application in universities, Ph.D. Thesis, Suleyman Demirel University, Institute of Social Sciences, Isparta. [33] J. Powers and P. McMullen, Using data envelopment analysis to select efficient large market cap securities, J. Bus. Manag. 7 (2000), no. 2, 31–42 [34] S. Saviz and E. Najafi, A new expansion on multi-stage DEA methodology in supply chain management, J. Ind. Manag. 8 (2013), no. 25, 18-28. [35] M. Sayadi and K. Azarbaijani, Technical efficiency estimation at public and private sector industries at Iran by using stochastic frontier analysis method, Bi-Quart. Sci.-Special. J. Dev. Plan. Econ. 1 (2012), no. 2, 33. [36] L.M. Seiford and J. Zhu, Profitability and marketability of the top 55 US commercial banks, Manag. Sci. 45 (1999), 1270–1288. [37] H. Soltanpanah, I. Dadashi and S. Zarei, The study of the efficiency levels of companies listed on the Tehran Stock Exchange based on the data envelopment analysis technique, Ind. Manag. Quart. 24 (2013). [38] K. Tone and M. Tsutsui, Dynamic DEA: A slacks-based measure approach, Omega 38 (2010), 3–4. [39] K. Tone and M. Tsutsui, Network DEA: A slacks-based measure approach, Eur. J. Oper. Res. 197 (2009), no. 43, 243–252. | ||
|
آمار تعداد مشاهده مقاله: 16,728 تعداد دریافت فایل اصل مقاله: 8,423 |
||